Purpose :
Speckle noise in optical coherence tomography (OCT) images is a granular noise that inherently exists and degrades the image quality. The challenge of conventional denoising methods is to distinguish the informational pattern from the speckle noise. The purpose of this study was to develop a novel optimization based method that estimated the noise-free OCT structures using the corresponding en face image as a reference.

Methods :
Twelve eyes of 12 healthy volunteers were scanned with a commercial OCT device (Cirrus HD-OCT, Zeiss, Dublin, CA) 10 times on the same day. The signal strength (SS) of the scanned images were controlled so as to vary SS from 1 to 10. We developed a fully automated OCT image denoising algorithm, which utilized signal decomposition and hybrid wavelet thresholding techniques via the minimization of a single objective function with multiple regularizations and constraints. The anatomical structures were penalized using a hybrid wavelet-domain sparsity and total variation (TV) regularization as to preserve the edges of retinal layers and to alleviate artifacts introduced by pure wavelet thresholding. To evaluate the performance of speckle reduction, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. BM4D, which is a patch-based transform-domain filtering method and the current standard for denoising medical images, and two conventional spatial domain filtering methods [including three dimensional Gaussian filtering and mixed median (on z axis) and Gaussian (on cross-sectional images) filtering] were used for comparison together with the raw volume.

Results :
All the processed volumes showed notable reduction of speckle without losing details in both en face and cross-sectional images (Figure 1). The proposed algorithm showed the highest SNR and CNR among all other denoising and smoothing methods (Figure 2). CNR was statistically significantly higher with the proposed method than BM4D at all SS level, while SNR showed significant difference only at SS=5.

Sample OCT cross-sectional images before and after processing (original SS=4). After processing, retinal layer structures become clearly visible with consistent texture for each different layers (arrow).

Sample OCT cross-sectional images before and after processing (original SS=4). After processing, retinal layer structures become clearly visible with consistent texture for each different layers (arrow).